System and method for hierarchical stream processing

ABSTRACT

A method, computer program product, and system for de-centralized stream processing is provided. The method may include providing a plurality of processing nodes each of said processing nodes configured to transmit and receive a stream of data. The method may also include providing a genome including a hierarchical set of tasks that represent a hierarchical functional decomposition of one or more problem scenarios. The method may further include identifying, at each of the processing nodes, one or more tasks for a first genome level. The method may additionally include differentiating, at one or more of the plurality of processing nodes, into one or more tasks, based upon, at least in part, the first genome level. The method may further include grouping a plurality of the processing nodes together to achieve a power level. The method may also include activating a second genome level once the power level has been reached. The method may further include repeating until one or more of the nodes differentiates into a further genome level.

BACKGROUND OF THE INVENTION

The growth of real-time digital systems, wireless sensor networks, anddistributed processing has seen the emergence of a class of applicationsthat focus on the continuous analysis of real-time data. This data takesnumerous forms, for example, audio, video, images, text, sensor data,and is typically subjected to complex processing. Stream processing is adistributed programming paradigm that has been shown to be suitable forprocessing massive amounts of continuous data in real-time. It is basedon the application of a series of processing operators to each elementin a continuous data set (or stream) rather than the sequentialprocessing of each data item.

While stream processing need not be implemented in a distributedfashion, it is in this domain that it is most often considered. The flowof data in a stream processing system can be represented in a dataflowgraph where each node in the graph models a processing task, or role,that may produce output streams of continuous data from the given inputstreams. Each of the processing roles would then be deployed to a set of(possibly distributed) processors. The existing literature on streamprocessing has mainly focused on a centralized application manager thatcan allocate each processing role to a suitable (possibly distributed)processor. A centralized determination of the role allocation iseffective in many scenarios and should facilitate a global optimizationof the role allocation.

However, as the volume of data, the number of processors, and thecomplexity of the dataflow graph all increase, a centralized approachwill potentially suffer scalability issues. This problem will beamplified if the number of processing nodes is constantly changing,which may be the case if the processing nodes are drawn from a volatileresource pool. Typical scenarios where this may arise would be streamprocessing systems that leverage the processor capability within a largenetwork of mobile phones, wireless sensors or the spare capacity withina network of fixed computers (as with the Berkeley Open Infrastructurefor Network Computing “BOINC” architecture).

BRIEF SUMMARY OF THE INVENTION

In a first embodiment, a method for de-centralized stream processing isprovided. The method may include providing a plurality of processingnodes each of said processing nodes configured to transmit and receive astream of data. The method may also include providing a genome includinga hierarchical set of tasks that represent a hierarchical functionaldecomposition of one or more problem scenarios. The method may furtherinclude identifying, at each of the processing nodes, one or more tasksfor a first genome level. The method may additionally includedifferentiating, at one or more of the plurality of processing nodes,into one or more tasks, based upon, at least in part, the first genomelevel. The method may further include grouping a plurality of theprocessing nodes together to achieve a power level. The method may alsoinclude activating a second genome level once the power level has beenreached. The method may further include repeating until one or more ofthe nodes differentiates into a further genome level.

One or more of the following features may be included. In someembodiments, grouping may utilize, at least in part, quorum sensing. Insome embodiments, each of the plurality of processing nodes may be incommunication with a subsection of a genome. In some embodiments,reaching a power level may be associated with obtaining a desired amountof processing power for a particular task. In some embodiments, each ofthe plurality of processing nodes may maintain one or more morphogenlevels associated with its parent processing node. In some embodiments,the morphogen levels may be associated with at least one of activationand inhibition. The method may further include load balancing betweenone or more of the plurality of processing nodes.

In a second embodiment, a computer program product may reside on acomputer readable storage medium and may have a plurality ofinstructions stored on it. When executed by a processor, theinstructions may cause the processor to perform operations includingproviding a plurality of processing nodes each of said processing nodesconfigured to transmit and receive a stream of data. Operations may alsoinclude providing a genome including a hierarchical set of tasks thatrepresent a hierarchical functional decomposition of one or more problemscenarios. Operations may further include identifying, at each of theprocessing nodes, one or more tasks for a first genome level. Operationsmay additionally include differentiating, at one or more of theplurality of processing nodes, into one or more tasks, based upon, atleast in part, the first genome level. Operations may further includegrouping a plurality of the processing nodes together to achieve a powerlevel. Operations may also include activating a second genome level oncethe power level has been reached. Operations may further includerepeating until one or more of the nodes differentiates into a furthergenome level.

One or more of the following features may be included. In someembodiments, grouping may utilize, at least in part, quorum sensing. Insome embodiments, each of the plurality of processing nodes may be incommunication with a subsection of a genome. In some embodiments,reaching a power level may be associated with obtaining a desired amountof processing power for a particular task. In some embodiments, each ofthe plurality of processing nodes may maintain one or more morphogenlevels associated with its parent processing node. In some embodiments,the morphogen levels may be associated with at least one of activationand inhibition. Operations may further include load balancing betweenone or more of the plurality of processing nodes.

In a third embodiment, a computing system is provided. The computingsystem may include at least one processor and at least one memoryarchitecture coupled with the at least one processor. The computingsystem may also include a first software module executable by the atleast one processor and the at least one memory architecture, whereinthe first software module may be configured to provide a plurality ofprocessing nodes each of said processing nodes configured to transmitand receive a stream of data. The computing system may also include asecond software module executable by the at least one processor and theat least one memory architecture, wherein the second software module maybe configured to provide a genome including a hierarchical set of tasksthat represent a hierarchical functional decomposition of one or moreproblem scenarios. The computing system may also include a thirdsoftware module executable by the at least one processor and the atleast one memory architecture, wherein the third software module may beconfigured to identify, at each of the processing nodes, one or moretasks for a first genome level. The computing system may also include afourth software module executable by the at least one processor and theat least one memory architecture, wherein the fourth software module maybe configured to differentiate, at one or more of the plurality ofprocessing nodes, into one or more tasks, based upon, at least in part,the first genome level. The computing system may also include a fifthsoftware module executable by the at least one processor and the atleast one memory architecture, wherein the fifth software module may beconfigured to group a plurality of the processing nodes together toachieve a power level. The computing system may also include a sixthsoftware module executable by the at least one processor and the atleast one memory architecture, wherein the sixth software module may beconfigured to activate a second genome level once the power level hasbeen reached. The computing system may also include a seventh softwaremodule executable by the at least one processor and the at least onememory architecture, wherein the seventh software module may beconfigured to repeat until one or more of the nodes differentiates intoa further genome level.

One or more of the following features may be included. In someembodiments, grouping may utilize, at least in part, quorum sensing. Insome embodiments, each of the plurality of processing nodes may be incommunication with a subsection of a genome. In some embodiments,reaching a power level may be associated with obtaining a desired amountof processing power for a particular application. In some embodiments,each of the plurality of processing nodes may maintain one or moremorphogen levels associated with its parent processing node. In someembodiments, the morphogen levels may be associated with at least one ofactivation and inhibition. The computing system may further include oneor more software modules configured to load balance between one or moreof the plurality of processing nodes.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will become apparent from the description, the drawings, andthe claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a diagrammatic view of a stream process coupled to adistributed computing network in accordance with the present disclosure;

FIG. 2 is a flowchart of the hierarchical stream process of FIG. 1 inaccordance with the present disclosure;

FIG. 3 depicts a diagram showing the concept of morphogenic spatialseparation in accordance with the present disclosure;

FIG. 4 depicts a dataflow graph consistent with the processes of thepresent disclosure;

FIG. 5 depicts a dataflow graph consistent with the processes of thepresent disclosure;

FIG. 6 depicts a dataflow graph consistent with the processes of thepresent disclosure;

FIG. 7 depicts a hierarchical genome configuration consistent with theprocesses of the present disclosure;

FIG. 8 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 9 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 10 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 11 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 12 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 13 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 14 depicts a table consistent with the processes of the presentdisclosure;

FIG. 15 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 16 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 17 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 18 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 19 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 20 depicts a diagram consistent with the processes of the presentdisclosure;

FIG. 21 depicts a diagram consistent with the processes of the presentdisclosure; and

FIG. 22 depicts a diagram consistent with the processes of the presentdisclosure.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIGS. 1 & 2, there is shown an embryonic stream process 10.As will be discussed below, embryonic stream process 10 may provide aplurality of processing nodes each of said processing nodes configuredto transmit and receive a stream of data. Embryonic stream process 10may add one or more new processing nodes to the computing system.Embryonic stream process 10 may determine a source node based upon, atleast in part, an activation level being above or below a particularthreshold. For each of the one or more added processing nodes, embryonicstream process 10 may automatically determine an appropriate role, basedupon, at least in part, a neighboring processing node.

The term “genome” as used herein may refer to the set of all availableroles that a node can differentiate into. The entire set of roles maydescribe all aspects of processing that the system will perform. Thegenome may also include the interrelations between the roles. Forexample, the output of Role A may be the input of Role B. The genome mayalso include any restrictions or promotions regarding thedifferentiation process for each node. For example, the genome may knowif Role A requires input from a camera on a node. If a particular nodedoes not have a camera, then it should never differentiate into Role A.

The term “role” or “task” as used herein may refer to a process thatmodifies (e.g., none to many) streaming input data to produce (e.g.,none to many) streaming output data. The set of all the roles of asystem may describe all of the processing that occurs on the system.

The term “node” or “cell” as used herein may refer to a processing unitthat may include a CPU, memory, I/O, etc. Some possible examples ofnodes may include, but are not limited to, sensors in a wireless sensornetwork, mobile phones, computers, or Micro Electronic MechanicalSystems (MEMS).

The term “stem cell” may refer to a node that may refrain fromdifferentiating into a particular role. Instead, it may remainundifferentiated waiting for a nearby node to fail. When the neighborfailure occurs, the stem cell may then differentiate to the role thathad the error, providing redundancy.

The term “activation” as used herein may refer to a variable that mayrepresent a virtual chemical that is produced self-catalyticallyActivation may be produced, diffused and decayed over time. The amountof activation produced may be proportional to the current activationsquared and may be inversely proportional to the amount of inhibitionpresent. Activation may diffuse slower then inhibition meaning its rangeof influence through diffusion may be limited when compared to the rangeof same amount of inhibition diffused.

The term “inhibition” may refer to a variable that may represent avirtual chemical that may be produced proportionally to the currentactivation squared. Inhibition may be produced, diffused and decayedover time. Inhibition may diffuse more rapidly then activation so it mayreach a greater number of roles through diffusion when compared to rangeof activation diffusion.

For the following discussion, server-side processes 10 will be describedfor illustrative purposes. It should be noted that client-side process12 may be incorporated into server-side process 10 and may be executedwithin one or more applications that allow for communication withclient-process 12. However, this is not intended to be a limitation ofthis disclosure, as other configurations are possible (e.g.,stand-alone, client-side automated testing processes and/or stand-aloneserver-side automated testing processes.) For example, someimplementations may include one or more of client-side processes 12, 14,16, 18 in place of or in addition to server-process 10.

The embryonic stream process may be a server-side process (e.g.,server-side process 10), a client-side process (e.g., client-sideprocess 12, client-side process 14, client-side process 16, orclient-side process 18), or a hybrid server-side/client-side process(e.g., the combination of server-side automated testing process 10 andone or more of client-side processes 12, 14, 16, 18).

Server-side embryonic stream process 10 may reside on and may beexecuted by server computer 20, which may be connected to network 22(e.g., the Internet or a local area network). Examples of servercomputer 20 may include, but are not limited to: a personal computer, aserver computer, a series of server computers, a mini computer, and/or amainframe computer. Server computer 20 may be a web server (or a seriesof servers) running a network operating system, examples of which mayinclude but are not limited to: Microsoft® IIS, Novell® Web Server, orApache® Web Server, for example.

The instruction sets and subroutines of server-side embryonic streamprocess 10, which may be stored on storage device 24 coupled to servercomputer 20, may be executed by one or more processors (not shown) andone or more memory architectures (not shown) incorporated into servercomputer 20. Storage device 24 may include but is not limited to: a harddisk drive; a tape drive; an optical drive; a RAID array; a randomaccess memory (RAM); and a read-only memory (ROM).

Server computer 20 may execute a web server application that allows foraccess to server computer 20 (via network 22) using one or moreprotocols, examples of which may include but are not limited to HTTP(i.e., HyperText Transfer Protocol), SIP (i.e., session initiationprotocol), and the Lotus® Sametime® VP protocol. Network 22 may beconnected to one or more secondary networks (e.g., network 26), examplesof which may include but are not limited to: a local area network; awide area network; or an intranet, for example.

Client-side processes 12, 14, 16, 18 may reside on and may be executedby client electronic devices 28, 30, 32, and/or 34 (respectively),examples of which may include but are not limited to personal computer28, laptop computer 30, a data-enabled mobile telephone 32, notebookcomputer 34, personal digital assistant (not shown), smart phone (notshown) and a dedicated network device (not shown), for example. Clientelectronic devices 28, 30, 32, 34 may each be coupled to network 22and/or network 26 and may each execute an operating system, examples ofwhich may include but are not limited to those described above.

The instruction sets and subroutines of client-side processes 12, 14,16, 18, which may be stored on storage devices 36, 38, 40, 42(respectively) coupled to client electronic devices 28, 30, 32, 34(respectively), may be executed by one or more processors (not shown)and one or more memory architectures (not shown) incorporated intoclient electronic devices 28, 30, 32, 34 (respectively). Storage devices36, 38, 40, 42 may include but are not limited to: hard disk drives;tape drives; optical drives; RAID arrays; random access memories (RAM);read-only memories (ROM); compact flash (CF) storage devices; securedigital (SD) storage devices; and memory stick storage devices.

Client-side processes 12, 14, 16, 18 and/or server-side embryonic streamprocess 10 may be processes that run within (e.g., are part of) asoftware testing system and/or application. Alternatively, client-sideembryonic stream processes 12, 14, 16, 18 and/or server-side embryonicstream process 10 may be stand-alone applications that work inconjunction with the software testing system and/or application. One ormore of client-side processes 12, 14, 16, 18 and server-side embryonicstream process 10 may interface with each other (via network 22 and/ornetwork 26).

Users 44, 46, 48, 50 may access server-side embryonic stream process 10directly through the device on which the client-side process (e.g.,client-side processes 12, 14, 16, 18) is executed, namely clientelectronic devices 28, 30, 32, 34, for example. Users 44, 46, 48, 50 mayaccess server-side embryonic stream process 10 directly through network22 and/or through secondary network 26. Further, server computer 20(i.e., the computer that executes server-side embryonic stream process10) may be connected to network 22 through secondary network 26, asillustrated with phantom link line 52.

The various client electronic devices may be directly or indirectlycoupled to network 22 (or network 26). For example, personal computer 28is shown directly coupled to network 22 via a hardwired networkconnection. Further, notebook computer 34 is shown directly coupled tonetwork 26 via a hardwired network connection. Laptop computer 30 isshown wirelessly coupled to network 22 via wireless communicationchannel 54 established between laptop computer 30 and wireless accesspoint (i.e., WAP) 56, which is shown directly coupled to network 22. WAP56 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n,Wi-Fi, and/or Bluetooth device that is capable of establishing wirelesscommunication channel 54 between laptop computer 30 and WAP 56.Data-enabled mobile telephone 32 is shown wirelessly coupled to network22 via wireless communication channel 58 established betweendata-enabled mobile telephone 32 and cellular network/bridge 60, whichis shown directly coupled to network 22.

As is known in the art, all of the IEEE 802.11x specifications may useEthernet protocol and carrier sense multiple access with collisionavoidance (i.e., CSMA/CA) for path sharing. The various 802.11xspecifications may use phase-shift keying (i.e., PSK) modulation orcomplementary code keying (i.e., CCK) modulation, for example. As isknown in the art, Bluetooth is a telecommunications industryspecification that allows e.g., mobile phones, computers, and personaldigital assistants to be interconnected using a short-range wirelessconnection.

The Stream Process

In some embodiments, stream process 10 may provide a plurality ofprocessing nodes each of said processing nodes configured to transmitand receive a stream of data. Stream process 10 may add one or more newprocessing nodes to the computing system. Stream process 10 maydetermine a source node based upon, at least in part, an activationlevel being above or below a particular threshold. For each of the oneor more added processing nodes, stream process 10 may automaticallydetermine an appropriate role, based upon, at least in part, aneighboring processing node.

As discussed above, the flow of data in a stream processing system maybe represented in a dataflow graph where each node in the graph models aprocessing task, or role, that may produce output streams of continuousdata from the given input streams. Each of the processing roles wouldthen be deployed to a set of (possibly distributed) processors. Theexisting literature on stream processing has mainly focused on acentralized application manager that can allocate each processing roleto a suitable (possibly distributed) processor. A centralizeddetermination of the role allocation is effective in many scenarios andshould facilitate a global optimization of the role allocation.

In some embodiments of the present disclosure, role allocation may bedistributed, thus allowing for a particular system to becomesignificantly more scalable, robust and autonomic. The teachings of thepresent disclosure allow for each processing node to be able toautonomously determine its optimal role selection, within constraintsthat guarantee optimized dataflow graphs, thus leading to a simpler andmore scalable architecture. This would be particularly suitable tomassive scale networks where node availability may be dynamic andnon-deterministic. This problem of creating a distributed dataflow maybecome more important as the number of digital devices, along with theamount of data they provide, increases worldwide. Some potentialapplications of a distributed role allocation may include, but are notlimited to, smart sensors, micro-electro-mechanical systems (MEMS), orswarming robots processing streaming data (video, audio andintelligence) where each node is autonomous yet contributing to theclustered computing power. The teachings of the present disclosure mayalso be applied to existing stream applications on mainframes wherenodes can self-organize to utilize shared memory, thus gaining thebenefits of robustness and scalability due to the distributed roleallocation.

In some embodiments, when new nodes (e.g., sensors, mobile phones,computers, etc.) are added to the system, they may automatically learnwhat role to take on (e.g., by looking at its neighboring nodes) inorder to be of the most benefit to the system. Since a streamapplication may be represented by a data flow graph, for an embryonicsolution this graph is part of the ‘genome’ of the system. In someembodiments, the genome may contain the relationships between roles andeach roles requirements (e.g. a particular role might require a camerasensor on the node). In some embodiments, the genome may refer to theset of all available processors or elements for a particularapplication. Each node in the system may know the whole genome and maydifferentiate into a suitable role to maximize efficiency based on whatroles are available on neighboring nodes.

In some embodiments, in order to separate the roles among the nodes, atechnique called morphogenesis may be used. Morphogenesis may be used todescribe how cells can autonomously differentiate with global knowledgefor biological pattern generation. Morphogenesis may be used to makesure the roles are separated while influencing neighbors to becomedesirable roles to improve efficiency. The embryonic stream processdescribed herein will be robust and scalable as there is no centralserver so it will have no single point of failure.

In some embodiments, morphogenesis may allow for autonomous nodes toseparate without any central control. Consider the spots on a leopard,for example, how a hair follicle cell knows whether to be light or darkbased on information gathered from neighboring cells through thechemical diffusion processes. A theory for this process is calledautocatalysis and long range inhibition and is based onreaction-diffusion mechanisms. The centre cell of a dark spot on aleopard's skin might be thousands of cells away from the edge of thespot, so a process of a decreasing gradient of chemicals calledmorphogens are passed from the centre cell so that all cells know howfar away they are from the source based on the morphogen concentrationat a given node.

The equations for morphogenic behavior for spacial separation aredescribed in H. Meinhardt, Models of Biological Pattern Formation,London: Academic Press, 1982. The two primary chemicals may includeactivation and inhibition.

In some embodiments, the following activation-inhibition equations maybe used:

$\begin{matrix}{\frac{\partial a}{\partial t} = {\frac{{sa}^{2} + C_{a}}{b} - {d_{a}a} + {D_{a}\frac{\partial^{2}a}{\partial x^{2}}}}} & {{EQN}\mspace{14mu} 1\text{:}} \\{\frac{\partial b}{\partial t} = {{sa}^{2} - {d_{b}b} + {D_{b}\frac{\partial^{2}b}{\partial x^{2}}}}} & {{EQN}\mspace{14mu} 2\text{:}}\end{matrix}$

In equations 1 and 2 above, “a” may refer to the activation chemicalconcentration and “b” may refer to the inhibition chemicalconcentration. “Ca” refers to a small constant activation production, sothe activation may become reignited in quiet sections of the graph. Therate of decay is given by “da” and “db” for the activation andinhibition respectively. The morphogens are diffused between neighborswith “Da” and “Db” being the constants for the laplacian diffusiontransformation of the activation and inhibition. The source density,“s”, may be constant or variable. As used herein, the term“autocatalysis” may refer to the activation morphogen that is created ona source node or a likely source node contender, which will furtherestablish its status as being a source node gaining high activationthrough a process of self-activation.

Referring now to FIG. 3, an embodiment 300 is provided depicting thebasic principals for spatial separation from morphogenesis. In thisparticular embodiment, the activation level is indicated by the boldoutline. When the activation is above a particular threshold, the nodemay becomes Role A. In this way, process 10 may automatically determinean appropriate role, based upon, at least in part, a neighboringprocessing node. As discussed herein, automatically determining mayinclude analyzing the activation level and an inhibition level.

In some embodiments, applying autocatalysis and long range inhibition toa set of nodes may produce a subset of source nodes that aregeographically evenly spaced. A node may become a source node when itsactivation is above a given threshold. In some embodiments, an approachfor solving differentiation in distributed dataflow is to allocate aseparate morphogen pair of activation and inhibition for each possiblerole in the dataflow. In this way, the morphogens may then be influencednot only by its own and neighboring levels of activation and inhibition,but also by the morphogens of other roles in the node. This may resultin multiple morphogens interacting with each other on the same node toinfluence desired inputs and outputs in the local neighborhood to findan optimal solution for a dataflow graph role distribution.

In some embodiments, one or more additional variables may be included tocomplement the existing morphogen equations (i.e., EQN 1 and EQN 2),namely desire, power and global inhibition. Each possible role on a nodewill have a value for activation, inhibition, desire and power, whileglobal inhibition is also maintained for the node but it is not rolespecific. Also, the source density of equations (2a, 2b) will bemodified to utilize these new variables.

$\begin{matrix}{s_{r} = {S_{c}\frac{q_{r}}{p_{r}}}} & {{EQN}\mspace{14mu} 3\text{:}}\end{matrix}$

where s_(r) is source density for role r, Sc is a constant, q_(r) isdesire, and p_(r) is power of role r. In words, the source density for arole is proportional to how much desire there is for that role over howmuch existing power is already present.

Desire (EQN 4) may cover the inter-node influence required to promotedesired roles on neighboring nodes.

$\begin{matrix}{{\frac{\partial q_{r}}{\partial t} = {{{- d_{q}}q_{r}} + {D_{q}\frac{\partial^{2}q_{r}}{\partial x^{2}}}}}{Where}} & {{EQN}\mspace{14mu} 4\text{:}} \\{\frac{\partial^{2}q_{r}}{\partial x^{2}} = {\frac{1}{\left| N_{n} \right|}{{\Sigma_{n_{z} \in N_{n}}\left( {q_{z.r} - q_{n.r}} \right)}.}}} & {{EQN}\mspace{14mu} 5\text{:}}\end{matrix}$

In some embodiments, the desire from neighbor z is given by q_(z,r). Itmay be calculated based on the neighbor's activation of each of thesupporting roles minus the existing power (p_(z,r)) of the role inquestion. This may be demonstrated by,

q _(z,r)=Σ_(tεR) _(r) (a _(z,t) −p _(z,r))(|R _(r)|)^(−1/2)  EQN 6

Here, Rr is the set of roles that are role-neighbors (inputs andoutputs) of role r in the genome. The activation for neighbor z of rolet is given by a_(z,t) and p_(z,r) is its power for role r. The desirevalue can be calculated on the neighboring nodes (Nn) and diffusedimmediately without these nodes needing to store it locally. The nodesin Nn will receive their own desire morphogens from their own diffusionprocess. Otherwise, a node will increase the desire for its inputs andoutputs on itself, potentially confusing its main role. To explain thisfurther, we shall revisit FIG. 4 as an example.

In some embodiments, some nodes in a system will become role A due to ahigh concentration of activation for A. These nodes can in turndistribute extra desire for Role B, as it is beneficial to have a B nodeclose to an A node. The A nodes will not increase the desire for B onitself, but only on their neighbors. Likewise, the B nodes willdistribute desire for both A and C, covering the desired inputs and theoutputs of Role B. Load balancing could also come into the desire (EQN4) equation since if a B node is getting overloaded, that means thatthere is a high demand for B in that area, and so it can send extradesire to get support for its excess demand. The neighbors desire,q_(z), is also partially normalized against roles with more connectionswithout favoring the endpoints, which would occur if it was completelynormalized.

2) Power

In some embodiments, power may refer to a linear measure of proximity toa source activation as opposed to inhibition that is the square ofactivation. Power defuses more slowly than activation giving it a widerinfluence. The power equation is similar to the source density equations(2g,2h). However, since source density has been changed to work formultiple role morphogens (EQN 3), and p_(r) is now inverselyproportional to source density (EQN 3), this variable is renamed to bepower.

EQN  7: $\begin{matrix}{\frac{\partial p_{r}}{\partial t} = {{Y\left( {a_{r} - p_{r}} \right)} - {d_{p}p_{r}} + {D_{p}\frac{\partial^{2}p_{r}}{\partial x^{2}}}}} & \left( {3e} \right)\end{matrix}$

3) Global Inhibition

In some embodiments, with multiple morphogens acting on the one node,there is the chance that a node could become highly active in multipleroles, hence inhibiting other neighbors to share the load. While a nodecan be limited to only be 1 role at a time, which would be determined bythe role morphogen that has the highest activation, it may still havehigh activation in other role morphogens which will hinder neighborsbecoming this role due to its long range inhibition. To mitigate thechance of 1 node becoming high in multiple morphogens, the originalmorphogen equation (EQN 2) needs to be modified to take into accountsome global inhibition. The term global meaning across all rolemorphogens in a node, not across all nodes in a graph.

The global inhibition is added to each roles local inhibition and theextra pressure makes it harder for multiple morphogens to have highvalues on the one node. Multiple morphogens to have high values on theone node.

b _(g) =G(s ₁ a ₁ ² +s ₂ a ₂ ²+ . . . )  EQN 8

In some embodiments, the extracted constant G provides inhibitiondampening. Note that the source density and activation are from the samerole, as opposed to some other equations where they are opposite. Thereason for this is because our modified source density (EQN 3) alreadyfactors in for the supporting role influence by accounting for desire(EQN 6). Equation 1 is modified to include the global inhibition and tomake activation and inhibition role specific.

$\begin{matrix}{\frac{\partial a_{r}}{\partial t} = {\frac{{sa}_{r}^{2} + C_{a}}{b_{r} + b_{g}} - {d_{a}a_{r}} + {D_{a}\frac{\partial^{2}a_{r}}{\partial x^{2}}}}} & {{EQN}\mspace{14mu} 9\text{:}}\end{matrix}$

A more thorough discussion of these equations may be found in “EmbryonicStream Processing Using Morphogens”, Nature and Biologically InspiredComputing (NaBIC) 2010, K. Sabir and D. Lowe.

In some embodiments, each node in the system may connect to other nodesin a web. The processing load may be distributed across all of the nodesin the system, without requiring the use of a central server (i.e.decentralized). This may mean that the nodes are relatively lightweight,as no single node has to know how to do all of the processing required.Also, in some embodiments, the roles may be rotated between the nodes ifneeded (e.g. to save on wear-and-tear, or to maximize battery life,etc.).

In some embodiments, new nodes may only need the address of (or aconnection to) one existing node in the system. From that, it may jointhe network, learn the genome, differentiate into a suitable role andstart processing. Each of the new processing nodes may be configured toidentify one or more tasks available to perform and to analyze one ormore of the existing processing nodes to identify a most suitable taskto perform.

In some embodiments, the inputs (i.e., sources) may be fixed (e.g. oneor more sensors getting temperature), and/or receiving inputs servedfrom a server, like SETI@home. Additionally and/or alternatively, theinputs could be related to the new nodes coming in (e.g. monitoring thevibration of your phone, when more phones are added to the network, theinputs increase).

Referring now to FIGS. 4-5, two diagrams (400, 500) depicting a streamsdataflow graph are shown. Diagram 400 may include a number of nodes, forexample, nodes 402 (A), 404 (B), 406 (C), and 408 (D). Any suitablenumber of nodes may be employed. Each node may correspond to aprocessor, which may be configured to perform a particular role.Information may pass between one or more of nodes 402-408. FIG. 5 showshow nodes 504 and 508 may differentiate into roles B and D to suit thegenome (e.g., the dataflow graph) to maximize efficiency by reducing thedistance between hops.

The Hierarchical Stream Process

In some embodiments, the method may include providing (100) a pluralityof processing nodes each of said processing nodes configured to transmitand receive a stream of data. The method may also include providing(102) a genome including a hierarchical set of tasks that represent ahierarchical functional decomposition of one or more problem scenarios.The method may further include identifying (104), at each of theprocessing nodes, one or more tasks for a first genome level. The methodmay additionally include differentiating (106), at one or more of theplurality of processing nodes, into one or more tasks, based upon, atleast in part, the first genome level. The method may further includegrouping (108) a plurality of the processing nodes together to achieve apower level. The method may also include activating (110) a secondgenome level once the power level has been reached. The method mayfurther include repeating (112) until one or more of the nodesdifferentiates into a further genome level.

As discussed above, the stream application may be represented by a dataflow diagram and this inter-role relationship may make up the genomealong with any specific requirements for roles (e.g. a role mightrequire the node has a camera sensor as an input). The genome may beshared across all of the nodes in the system, and each node mayautonomously differentiate into an appropriate role based on the rolesof its neighborhood (e.g. its nearest nodes). Each of the plurality ofprocessing nodes maintain One or more morphogen levels associated withits parent processing node.

In some embodiments, the teachings of the present disclosure may beapplied to any number of suitable applications. Some of these mayinclude, but are not limited to, distributed sensor networks, phoneapplications, mainframes or even smart dust. Here, the term “smart dust”may refer to one or more small sensors known as motes made from MicroElectro Mechanical Systems (MEMS) or Nano Electro Mechanical Systems(NEMS). These may be cheap, autonomous and small enough to be able to bepainted onto walls.

In some embodiments, as the application becomes more complex,scalability issues may arise. For example, if the genome is 10 roles,then each node in the system will have a copy of the genome and its 10roles so it may determine which one to differentiate to. However, as thegenome gets more complex, say 10,000 roles (e.g. a MEMS applicationsimulating a desktop computer on a wall, splitting into CPU, Memory,Storage, etc), then it may become unfeasible for each node to know theentire genome (especially when considering the memory constraints of aMEMS device).

Also, in some embodiments, if a node's neighbors have already alldifferentiated into the first 50 out of 10,000 roles, then it may beinefficient for that node to know the entire genome. It may be possibleto optimize network traffic by having each node select nearby roles, asit is likely only going to differentiate into a possible first ˜50-100roles.

In some embodiments, the hierarchical embryonic stream process describedherein may be used to break a particular genome into a hierarchy forembryonic computing where each node only knows a subset of the genome.For example, at the top of the genome hierarchy may be a generic processdescribing the whole system. The lower levels of the genome hierarchymay include very specific processes. Nodes may start at the top processand unify with neighboring nodes until they have enough power to triggerthe next level of the genome at which point they may differentiate intothe different roles at the lower genome level. In this way, each of theplurality of processing nodes may be in communication with a subsectionof a genome.

In some embodiments, each genome level may have a minimum powerrequirement which may be needed by nodes that have taken on that role.Reaching a power level may be associated with obtaining a desired amountof processing power for a particular task. For this minimum power to bereached, a group of nodes may need to gather in ‘unity’ to all belong tothat given role. Once this power requirement has been reached, then thenext level of the genome under that role is triggered. As the lowerlevels are triggered, more processes may be available for the nodes todifferentiate into, hence the nodes may specialize over time. Thisprocess may then repeat so the nodes may eventually evolve into acomplex application.

In some embodiments, and apart from the benefits of memory managementand application complexity scalability, the teachings of the presentdisclosure may make it simpler for the developer to comprehend how thesystem is operating through the use of information hiding. For example,each genome level may be broken down to a number of distinct roles whichmay be easier to comprehend then looking at a flat 10,000 role genome.

There are similarities found in biology through some genes being able toactivate whole pages of other genes, in effect simulating informationhiding. The hierarchical genome from a biological viewpoint is alsodiscussed in the book: Gordon, R., The Hierarchical Genome andDifferentiation Waves, Novel Unification of Development, Genetics andEvolution, Singapore: World Scientific Publ. Co., 1999 A. G. DesnitskiiBiological Research Institute, St. Petersburg State University.

Referring now to FIG. 6, an embodiment 600 is provided depicting adiagram where each node knows the entire genome. The top circlesrepresent the genome data flow diagram 602. In this particular example,data flow diagram 602 is a trivial sequential diagram. The bottom nodes604 may represent the actual nodes in the system (e.g., sensors, phones,motes, cells in a blue gene, etc).

Referring now to FIG. 7, a diagram 700 of an embodiment is provideddepicting a hierarchical genome where each node only knows a subsectionof what is relevant from the genome. For example, diagram 700 includespartial genomes or subsets 702 a-f. Each node only knows its partialgenome or subset, e.g., nodes 704 c know partial genome 702 c, nodes 704d know partial genome 702 d, etc.

Referring now to FIGS. 8-11, a diagram 800 is provided depicting toplevel genome 802. In this particular embodiment, top level genome 802appears as just one role. Diagram 800 also includes a number ofundifferentiated nodes 804. Node 806 represents a node that hasdifferentiated to the role of top level genome 802. As shown in FIG. 8,this may begin with one cell and individual nodes may replicate andspread the genome, gathering together until there is enough processingpower to fulfill the application. In this way, and in accordance withthe present disclosure, each individual node may receive a genome level,differentiate into roles from that genome level, gather the minimumpower for a role by grouping together (e.g., unified group 906 shown inFIG. 9), and activate the next genome level for that role.

Referring now to FIG. 10, a diagram 1000 is provided depicting thetransition to the next level genome 1002. In some embodiments, thehierarchical embryonic stream process 10 described herein may utilizequorum sensing as part of the grouping and decision making process. Inthis way, each individual component or node may have a means ofassessing the number of other components or nodes that they interactwith and may also have a standard response once a threshold number ofcomponents is detected.

Referring now to FIG. 10, diagram 1000 shows the evolution from the toplevel genome (e.g. basic genome level 0) to the next level. As shown inthe figure, as the next level evolves it may differentiate into one ormore sub-roles, as indicated by level 1 A 1004, level 1 B 1006, level 1C 1008, and level 1 D 1010. Again, it should be noted that the teachingsof the present disclosure may be utilized in a variety of applications.For example, in the smart dust scenario discussed above, level 1 A 1004may correspond to a CPU, level 1 B 1006 may correspond to a hard drive,level 1 C 1008 may correspond to an application space, and level 1 D1010 may correspond to some I/O.

Referring now to FIG. 11, diagram 1100 shows the nodes grouping togetherand differentiating. In some embodiments, the nodes may group togetherto satisfy an existing genome level. Once any group has reached itsdesired power, the next genome may be activated for that particulargroup. FIG. 12 depicts a diagram 1200 showing an embodiment after thenodes continue to specialize. Each node may maintain morphogen levelsfor its parents, which were previously calculated as well as its currentgenome set. In some embodiments, each of the plurality of processingnodes may maintain one or more morphogen levels associated with itsparent processing node. In some embodiments, the morphogen levels may beassociated with at least one of activation and inhibition.

As discussed above, each possible role of the genome may have amorphogen pair (e.g., activation and inhibition). With a hierarchicalgenome, a node may be operating multiple roles, e.g., a top level role,a child role of that, a child role of that, etc. The higher level rolesmay continue to function after its child genome level has been triggeredand may still follow the morphogenesis laws of auto-catalytic activationand long range inhibition. However, since they have reached a certainlevel of power and hence stability, they will not be re-differentiatingas rapidly as the lower level roles.

In some embodiments, since the higher level roles still operate, thereis a chance of high level load balancing between nodes that exist on theboundaries of child roles (e.g. for node failure or a change in inputload). When a higher level role differentiates to load balance, then theexisting child genome levels can be discarded as a new child genomelevel will be obtained.

Referring now to FIG. 13, a diagram 1300 is depicted showing loadbalancing between one or more of the plurality of processing nodes. Morespecifically, diagram 1300 shows roles 1304 c being under load so theyget help from the roles 1304 d. A node from 1304 d becomes a node from1304 c and it discards its old genome level (see the lines connecting1304 c and 1302 d) and obtains the new genome level (see the lines from1304 c to 1302 c).

Referring now to FIG. 14, a table 1400 is provided including adescription of morphogen levels on a node that has differentiated into A1 from FIG. 12. At each level of the genome, some nodes (known as genomestore nodes) may have the task of holding all of the sub genome levels.This way, each node may only need to know the current genome level ofroles for possibilities of what it could differentiate into. Once thegenome level's power has been triggered, then the next genome level isretrieved from the storage nodes, and the nodes may differentiate tosatisfy the new level. Once nodes have unified to gather enough powerfor the child genome level, then new genome store nodes are created outof these unified nodes that may hold the child genome level and all itschildren genome levels.

Referring now to FIG. 15, a diagram 1500 is provided depicting anembodiment including a meta genome level operating system (OS) 1502. Insome embodiments, meta genome level OS 1502 may be created at everygenome level. Meta genome level OS 1502 may provide a datastore to holdthe entire genome and to keep track of input and output node identities.In some embodiments, some nodes in a genome level may be set aside tokeep track of all inputs and outputs IDs in the genome level, known asI/O store nodes 1504. This redundancy may cover communication betweenthe genome levels and allow for protection in the event that theexisting nodes handling input and output fail. The genome store nodes1506 and the I/O store nodes 1504 together may be seen as metadata for agenome level and may act like a simple operating system for that level.This type of configuration may be used, for example, if the genome istoo large for a given device. In this way, a distributed database may beused for storage purposes (e.g. a few nodes may store the genome). Thegenome roles for the current level 1503 may be in communication with I/Ostore nodes 1504 and genome store 1506.

The Totipotent Morphogenic Stem Cell Process

In some embodiments, issues may arise concerning load balancing androbustness in a morphogenic system. For example, when a systemstabilizes, a given node may have a high activation for its currentrole, and a high inhibition for all the roles it is not. When a nodefails, there may be a delay in when the inhibition on nearby nodes candecay to a low enough level that another node can take up this role.Also, the other nodes may already be busy doing their own roles. In thisparticular case, if one is to take up the role of the node that died,then its own role may need to be shuffled to other nodes. Therestructuring of many nodes roles may be inefficient and possibly slowfor the system to reach stability again.

Nature accounts for cell robustness through the use of stem cells thatare toti-potent, meaning they can differentiate into any type of cell.In this way, in some embodiments, the method may include providing aplurality of processing nodes each of said processing nodes configuredto transmit and receive a stream of data. The method may further includerestricting a subset of the plurality of processing nodes fromdifferentiating into a role. The method may also include identifying afailure at one of the processing nodes and replacing the failed nodewith one of the processing nodes from the restricted subset.

In other words, the totipotent process described herein may restrictsome (e.g. 10%) of the nodes from differentiating into roles, and thesenodes may wait for node failure, and then immediately differentiate intothe required role. These nodes in waiting may be similar to stem cellsin adult organisms, where they delay differentiating to handle failover.As used herein, these nodes in the stream processing domain may bereferred to as morphogenic stem cells (“MSC”). The benefit of using MSCis that because they are not accepting inhibition through diffusion,they may rapidly differentiate once they are required to handle thefailover. In some embodiments, morphogenesis may be used to determinethe spatial placement of the MSC in the system.

While the concept of redundancy for self-healing is known in embryonicsystems (see a hardware example in “Reliability Analysis inSelf-Repairing Embryonic Systems”, Cesar Ortega and Andy Tyrrell,Department of Electronics University of York), the present disclosureuses morphogenesis to determine which nodes will become stem cells. Thebenefit of using morphogenesis is that the MSC placement may not bepre-fixed, and it may fluctuate depending on the genome size, theclustering coefficient (e.g., how ‘dense’ the network is), the criticalnature of the role and the average failure time.

Also, using morphogenesis may allow for new MSC's to be created from themost under-utilized roles. Therefore, the system is self-healing due tothe use of MSC, and its repair mechanism is also self-healing (e.g., inthe creation of new MSC's).

In some embodiments, the MSC may spatially separate by using morphogenicequations that support self-catalytic activation and long rangeinhibition. The equations for morphogenic behavior for spatialseparation are described above in Equations 1 and 2. (see Sabir, K. andLowe, D., “Embryonic stream processing using morphogens”, Nature andBiologically Inspired Computing (NaBIC), 2010 Second World Congress on,vol., no., pp. 603-610, 15-17 Dec. 2010). In this way, a restrictedsubset of processing nodes may be spatially separated throughout aplurality of nodes. In some embodiments, spatial separation may be basedupon the activation level and/or the inhibition level.

In some embodiments, the activation constant Ca from Equation 1 may beinfluenced by various criteria, including, but not limited to, genomesize, the clustering coefficient (e.g., how ‘dense’ the network is), thecritical nature of the role and the average failure time. For example,if a role is critical, it may have a higher density of MSC's present inits neighborhood.

In some embodiments, the morphogens used for MSC and the role morphogensdetailed above may differ in that while role morphogens are influencedby other role morphogens, the MSC morphogens are completely independentof the role morphogens. This may allow it to have no pre-existinginhibition at the time it needs to differentiate for failover. Inbiological terms, it is simulating the instant birth of a new cell inbiological terms.

In some embodiments, once an MSC has been activated for failover, it mayact like a normal new node in the morphogenesis process, so if a rolehas disappeared, then there may be a high desire for that role promotingthe MSC to differentiate to that role. Also, once an MSC hasdifferentiated, then that may create a demand for a new MSC in thatarea. While the MSC morphogens are not affected by role morphogens, theymay be affected by a nodes current load. This may stop a node underheavy load from stopping its work and differentiating into an MSC, andit may promote nodes under light load to be candidates to become a newMSC. In other words, an under utilized node may be added to the subsetafter a failed node has been replaced.

Creating demand for a new MSC seems like it is revisiting the originalproblem of inefficient restructuring of node's roles. However, itdiffers in that MSC may allow for rapid differentiation due to nopre-existing inhibition. This extra redundancy may allow for theapplication to respond rapidly to failover. Additionally and/oralternatively, the most under-utilized node in the area may become thenew MSC, and since there may be no dependencies between role morphogensand MSC's, there may be less ‘role shuffling’ required as there are nodesired neighbors for a MSC. The process to replenish a MSC is not astime critical as normal failover.

In some embodiments, MSC's may be able to determine and/or identify whena fail has occurred due to the excess of the desire morphogen and lackof power for a given role in the system. In this way, enough MSC's needto be present in the system so the differentiated nodes can adequatelyhandle the load. This architecture may also allow for MSC's todifferentiate to help in times of peak load, where the existingprocessing power is not enough.

The Multipotent Morphogenic Stem Cell Process

While the totipotent MSC's described above could exist and functionwithin a hierarchical genome, there is a chance that due to the numberof roles co-existing per node (including the parent roles and the chosendifferentiated child role), it could be possible that a high demand forstem cells for one genome level may reduce the robustness of othergenome levels as the stem cells are already used up. This may hinder agenome level's ability to act autonomously as failures in other genomelevels may directly affect its ability to self-heal.

In some embodiments, the multipotent morphogenic stem cell processdescribed herein may add capability to the hierarchical genome conceptby adding MSC for each level of the genome. Where instead of each MSCbeing totipotent (i.e., being able to differentiate to any role), eachgenome level may have its own MSC's. So each MSC at each genome levelmay only differentiate into roles for that genome level (meaning the MSCis multipotent).

In some embodiments, the method may include providing a plurality ofprocessing nodes in a hierarchical genome having a plurality of levels,wherein each of said processing nodes is configured to transmit andreceive a stream of data. The method may further include restricting asubset of the plurality of processing nodes from differentiating into arole within each level of the hierarchical genome. The method may alsoinclude identifying a failure at one of the processing nodes andreplacing the failed node with one of the processing nodes from therestricted subset.

In some embodiments, the MSC's may act as a restricted subset of theprocessing nodes that may be spatially separated within each level ofthe hierarchical genome. This spatial separation may be based upon oneor more of an activation level and an inhibition level from the parentgenome level. After the failed node has been replaced a new node may beadded to the restricted subset, thus supplying a replacement MSC. Thisadded node may be an under-utilized, or relatively under-utilized node.In some embodiments, each genome level may maintain a separaterestricted subset of nodes configured to handle node failure for thegenome level. The processes described herein may be configured toidentify a failure by determining one or more of a desire level and apower level. Each unrestricted node may include an activation level andan inhibition level as is discussed above.

In biology, the stem cell differentiation potency is classified in orderof decreasing potency as totipotent, pluripotent, multipotent,oligopotent and unipotent. Since streams processing applications arediverse in both application complexity and topology size, the presentdisclosure shall arbitrarily call all MSC in a hierarchical genome asmultipotent, as they all operate in the same way, in that they can onlydifferentiate into roles from their genome level. (Other naming optionswould be pluripotent or oligopotent).

In some embodiments, the distribution of multipotent MSC may be similarto that described above with regards to totipotent MSC using selfcatalytic activation and long range inhibition. However, a genome levelmay have its own distributions of MSC's. In some embodiments, thetrigger for a multipotent MSC to activate may be the same as atotipotent MSC, that is large desire and little power for a particularrole (in this case, a role limited to its genome level).

In some embodiments, a multipotent MSC may not accept role morphogensfrom its own genome (or child genome) level. However, it may accept rolemorphogens from its parent, genome level. This may allow the MSC to comepre-differentiated to its parent genome level so it can help its owngenome level immediately when required.

Referring now to FIGS. 16-22, a series of diagrams are provideddepicting various embodiments of the multipotent MSC process. In thediagrams, the top dataflow datagram refers to a particular genome levelfor a hierarchical genome. The bottom group of circles represents theactual topology, where the color reflects the role chosen for that node.Multipotent MSC's even work for the top genome level (though in atrivial manner) as FIGS. 16-18 describe.

FIG. 16 depicts a diagram 1600 showing a basic genome level 0. In orderto trigger the next genome level the minimum power requirements may needto be met. In this particular example, the minimum power requirement is128. Once the minimum power requirements have been met, upon the failureof a node the minimum requirements are no longer satisfied as is shownin FIG. 17. However, if stem cells were also part of the minimumrequirements calculation, then redundancy is built in. In thisparticular example, there may be only one type of role the MSC candifferentiate into. These may be referred to as Level 0 stem cells (“S”)as is shown in FIG. 18. Here, (“N”) may represent a new cell from a stemcell.

Referring now to FIGS. 19-20, a diagram 1900 is shown depicting the nextgenome level (i.e. Level 1). As shown in the Figure, at the next genomelevel (i.e., Level 1), both types of stem cells are in operation,namely, Level 0 Stem cells and Level 1 stem cells. In some embodiments,a percentage of the Level 0 nodes may refrain from the next level ofdifferentiation, and instead become Level 1 stem cells. FIG. 20 depictsa diagram 2000 showing a failure of one of the nodes, at this point aLevel 1 stem cell may replace the failed node. In some embodiments, theLevel 1 stem cells may not receive the Level 1 role morphogens (i.e., A,B, C, D) however they may receive the Level 0 role morphogens.

Referring now to FIGS. 21-22, a diagram 2100 is shown depicting the nextgenome level (i.e. Level 2). In some embodiments, the Level 2 stem cellsfor A may not receive Level 2 role morphogens (i.e., A1, A2, A3) howeverthey may receive the Level 0 & Level 1 (i.e., A, B, C, D) rolemorphogens. This means that as soon as there is a failure in Level 2 forA (e.g. A1 dies) then there is a new cell (formerly a stem cell) that isalready differentiated to A, yet it has no A1 inhibition to slow itdown. These two criteria will ensure a rapid differentiation to replacethe dead A1. FIG. 22 depicts a diagram 2200 showing a contained failure.The failure shown in FIG. 22 may temporarily use up all Level 2 stemcells for A, however, it may not directly effect on the self-healingcapabilities of Level 1 roles, or of other Level 2 roles for B, C, andD.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, apparatus, method or computerprogram product. Accordingly, aspects of the present invention may takethe form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java™, Smalltalk™, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer (i.e., a client electronic device), partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server (i.e., a server computer). In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).

Aspects of the present invention may be described with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and/or computer program products according to embodiments ofthe invention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures may illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. Further, one or moreblocks shown in the block diagrams and/or flowchart illustration may notbe performed in some implementations or may not be required in someimplementations. It will also be noted that each block of the blockdiagrams and/or flowchart illustration, and combinations of blocks inthe block diagrams and/or flowchart illustration, can be implemented byspecial purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

A number of embodiments and implementations have been described.Nevertheless, it will be understood that various modifications may bemade. Accordingly, other embodiments and implementations are within thescope of the following claims.

1. A computer-implemented method for de-centralized stream processing,the method comprising: providing a plurality of processing nodes each ofsaid processing nodes configured to transmit and receive a stream ofdata; providing a genome including a hierarchical set of tasks thatrepresent a hierarchical functional decomposition of one or more problemscenarios; identifying, at each of the processing nodes, one or moretasks for a first genome level; differentiating, at one or more of theplurality of processing nodes, into one or more tasks, based upon, atleast in part, the first genome level; grouping a plurality of theprocessing nodes together to achieve a power level; activating a secondgenome level once the power level has been reached; and repeating untilone or more of the nodes differentiates into a further genome level. 2.The method of claim 1, wherein grouping utilizes, at least in part,quorum sensing.
 3. The method of claim 1, wherein each of the pluralityof processing nodes is in communication with a subsection of a genome.4. The method of claim 1, wherein reaching a power level is associatedwith obtaining a desired amount of processing power for a particulartask.
 5. The method of claim 1, wherein each of the plurality ofprocessing nodes maintains one or more morphogen levels associated withits parent processing node.
 6. The method of claim 5, wherein themorphogen levels are associated with at least one of activation andinhibition.
 7. The method of claim 1, further comprising: load balancingbetween one or more of the plurality of processing nodes.
 8. A computerprogram product residing on a computer readable storage medium having aplurality of instructions for de-centralized stream processing, which,when executed by a processor, cause the processor to perform operationscomprising: providing a genome including a hierarchical set of tasksthat represent a hierarchical functional decomposition of one or moreproblem scenarios; identifying, at each of the processing nodes, one ormore tasks for a first genome level; differentiating, at one or more ofthe plurality of processing nodes, into one or more tasks, based upon,at least in part, the first genome level; grouping a plurality of theprocessing nodes together to achieve a power level; activating a secondgenome level once the power level has been reached; and repeating untilone or more of the nodes differentiates into a further genome level. 9.The computer program product of claim 8, wherein grouping utilizes, atleast in part, quorum sensing.
 10. The computer program product of claim8, wherein each of the plurality of processing nodes is in communicationwith a subsection of a genome.
 11. The computer program product of claim8, wherein reaching a power level is associated with obtaining a desiredamount of processing power for a particular task.
 12. The computerprogram product of claim 8, wherein each of the plurality of processingnodes maintains one or more morphogen levels associated with its parentprocessing node.
 13. The computer program product of claim 12, whereinthe morphogen levels are associated with at least one of activation andinhibition.
 14. The computer program product of claim 8, furthercomprising: load balancing between one or more of the plurality ofprocessing nodes.
 15. A computing system for de-centralized streamprocessing comprising: at least one processor; at least one memoryarchitecture coupled with the at least one processor; a first softwaremodule executable by the at least one processor and the at least onememory architecture, wherein the first software module may be configuredto provide a plurality of processing nodes each of said processing nodesconfigured to transmit and receive a stream of data; a second softwaremodule executable by the at least one processor and the at least onememory architecture, wherein the second software module may beconfigured to provide a genome including a hierarchical set of tasksthat represent a hierarchical functional decomposition of one or moreproblem scenarios; a third software module executable by the at leastone processor and the at least one memory architecture, wherein thethird software module may be configured to identify, at each of theprocessing nodes, one or more tasks for a first genome level; a fourthsoftware module executable by the at least one processor and the atleast one memory architecture, wherein the fourth software module may beconfigured to differentiate, at one or more of the plurality ofprocessing nodes, into one or more tasks, based upon, at least in part,the first genome level; a fifth software module executable by the atleast one processor and the at least one memory architecture, whereinthe fifth software module may be configured to group a plurality of theprocessing nodes together to achieve a power level; a sixth softwaremodule executable by the at least one processor and the at least onememory architecture, wherein the sixth software module may be configuredto activate a second genome level once the power level has been reached;and a seventh software module executable by the at least one processorand the at least one memory architecture, wherein the seventh softwaremodule may be configured to repeat until one or more of the nodesdifferentiates into a further genome level.
 16. The computing system ofclaim 15, wherein grouping utilizes, at least in part, quorum sensing.17. The computing system of claim 15, wherein each of the plurality ofprocessing nodes is in communication with a subsection of a genome. 18.The computing system of claim 15, wherein reaching a power level isassociated with obtaining a desired amount of processing power for aparticular task.
 19. The computing system of claim 15, wherein each ofthe plurality of processing nodes maintains one or more morphogen levelsassociated with its parent processing node.
 20. The computing system ofclaim 19, wherein the morphogen levels are associated with at least oneof activation and inhibition.